Abstract
Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80,286 microprocessor for a smart city. The proposed system consists of ‘5’ phases, namely IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data are collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80,286 microprocessor. An efficient OWENN algorithm for traffic prediction and traffic signal control using a Intel 80,286 microprocessor for a smart city. After extracting the features, the classification is performed in this step. Hereabout, the classification is done by using the optimized weight Elman neural network (OWENN) algorithm that classifies which places have more traffic. OWENN attains 98.23% accuracy than existing model also its achieved 96.69% F-score than existing model. The experimental results show that the proposed system outperforms state-of-the-art methods.
Highlights
The current high level of increase in the number of vehicles without additional supporting transportation infrastructure is a major problem for the smart city growth
The performance of the proposed Optimized Weight Elman Neural Network (OWENN) algorithm is compared with some traditional techniques, namely, Elman Neural Network (ENN), Convolutional Neural Network (CNN), Neural Network (NN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques
The scheme utilises four estimation metrics used for classification problems to assess the efficiency of the proposed procedure, i.e. precision, fmeasurement, mean absolute error (MAE) and Root mean square error (RMSE)
Summary
The current high level of increase in the number of vehicles without additional supporting transportation infrastructure is a major problem for the smart city growth. Owing to the pollution and traffic disruptions generated by these mechanisms (signal control), traffic management and logistics are often major problems that must be dealt with. The traffic signals controls device looked to be utilised as a traffic management system, but that it is being used as a smart city's traffic management system, it plays an important part in traffic safety [2]. As one of the main inputs for understanding and managing traffic, traffic data collections are quite important. Traffic congestion detection (i.e. traffic management) is one of the problems in the efficient traffic signal control system. If there is no initiative, this dilemma would be unmanageable [5, 6]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
